INTENT DETECTION

InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)

Challenge:	Current intent detection work experiments with minor intent categories.
Approach:	They propose a retrieval-augmented generation framework that uses query-to-query and query- to-metadata approaches to retrieve intents from metadata.
Outcome:	The proposed framework improves on query-to-query (Q2Q) and query- to-metadata (Q 2M) approaches.

Copied to clipboard

Prakhar Gupta, Cathy Jiao, Yi-Ting Yeh, Shikib Mehri, Maxine Eskenazi, Jeffrey Bigham

GameTox: A Comprehensive Dataset and Analysis for Enhanced Toxicity Detection in Online Gaming Communities (2025.naacl-short)

Challenge:	Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks.
Approach:	They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks.
Outcome:	The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection.

Copied to clipboard

Usman Naseem, Shuvam Shiwakoti, Siddhant Bikram Shah, Surendrabikram Thapa, Qi Zhang

A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)

Challenge:	Existing methods to detect toxic behavior in online gaming environments are limited by utterance-level annotation.
Approach:	They propose to annotate game chat utterances for toxicity detection through intent classification and slot filling.
Outcome:	The proposed model improves the detection of toxic speech in online gaming environments and reveals limitations of current models.

Copied to clipboard

Yu Wang, Yilin Shen, Hongxia Jin

Investigating the Role and Impact of Disfluency on Summarization (2023.emnlp-industry)

Challenge:	Intent detection and slot filling are two main tasks for building a spoken language understanding system.
Approach:	They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly.
Outcome:	The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data.

Copied to clipboard

Varun Nathan, Ayush Kumar, Jithendra Vepa

Multimodality

Challenge:	Existing studies have focused on disfluency detection and removal, with limited studies into its impact on downstream tasks.
Approach:	They propose to incorporate disfluency in summarization models to reduce the impact of replacement disfluencies on natural language processing tasks.
Outcome:	The proposed model improves on both public and real-life datasets and shows that it can handle disfluent data with up to 6.99-point degradation in Rouge-L score and replacement disfluencies have the highest negative impact.

Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog (N19-1)

Copied to clipboard

Rashmi Gangadharaiah, Balakrishnan Narayanaswamy

Information Retrieval

Challenge:	Neural network models have gained traction for sentence-level intent classification and token-based slot-label identification.
Approach:	They propose a neural network model that performs multi-label classification for identifying multiple intents and produces token-based slot-l labels at the token-level.
Outcome:	The proposed model provides a small but statistically significant improvement on the ATIS dataset and 55% accuracy improvement on an internal multi-intent dataset.

Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (2021.naacl-main)

Copied to clipboard

Jie Wu, Ian Harris, Hongzhi Zhao

RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling (2020.emnlp-main)

Challenge:	Recent research shows promising results by jointly learning of slot filling and intent detection tasks.
Approach:	They propose a way to combine slot filling and slot filler learning to achieve state-of-the-art results.
Outcome:	The proposed model outperforms existing methods on benchmark datasets and ATIS datasets.

Copied to clipboard

Jun Quan, Shian Zhang, Qian Cao, Zizhong Li, Deyi Xiong

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues spanning over 12 domains . despite of substantial progress made, there are challenges in creating challenging datasets in terms of size, multiple domains, semantic annotations and complexity.
Approach:	They propose a large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations that captures discourse phenomena for task-oriented dialogue modeling.
Outcome:	The proposed dataset contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues with more than 150K utterances spanning over 12 domains.

Copied to clipboard

Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022.findings-naacl)

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

Copied to clipboard

Vaibhav Varshney, Mayur Patidar, Rajat Kumar, Lovekesh Vig, Gautam Shroff

ConvFiT: Conversational Fine-Tuning of Pretrained Language Models (2021.emnlp-main)

Challenge:	Existing methods to identify all possible user intents at design time are expensive and require storage of past data.
Approach:	They propose to continually train an intent detector on new intents while maintaining performance on prior intents.
Outcome:	The proposed method outperforms exemplar replay-based approaches on lifelong intent detection tasks and achieves state-of-the-art on four public datasets.

Copied to clipboard

Ivan Vulić, Pei-Hao Su, Samuel Coope, Daniela Gerz, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Tsung-Hsien Wen

CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)

Challenge:	Existing Transformer-based language models (LMs) are not effective as sentence encoders when used off-the-shelf.
Approach:	They propose a method which turns a pretrained LM into a universal conversational encoder and task-specialised sentence encoder.
Outcome:	The proposed framework achieves state-of-the-art ID performance across the board with particular gains in the most challenging, few-shot setups.

Copied to clipboard

Yijin Liu, Fandong Meng, Jinchao Zhang, Jie Zhou, Yufeng Chen, Jinan Xu

Challenge:	Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance.
Approach:	They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner.
Outcome:	The proposed network outperforms existing models on two benchmarks and a self-collected corpus.

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information (2023.eacl-main)

Copied to clipboard

Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim, Sungjin Lee, Devamanyu Hazarika, Mahdi Namazifar, Di Jin, Yang Liu, Dilek Hakkani-Tur

Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation (2025.findings-emnlp)

Challenge:	Intent detection is a fundamental element in task-oriented dialogue systems, usually occurring within the Natural Language Understanding component.
Approach:	They propose an in-context data augmentation approach that fine-tunes a pre-trained language model and synthesizes new datapoints that correspond to given intents.
Outcome:	The proposed method produces training data that achieves state-of-the-art on three challenging intent detection datasets and performs on par with the state- of-the art in full-shot settings.

Copied to clipboard

Jun Zhuang, Haibo Jin, Ye Zhang, Zhengjian Kang, Wenbin Zhang, Gaby G. Dagher, Haohan Wang

Intent Detection in the Age of LLMs (2024.emnlp-industry)

Challenge:	Prior work has shown that intent detection enhances LLMs’ moderation guardrails, but the robustness of these guardrail mechanisms under malicious manipulations remains under-explored.
Approach:	They propose a two-stage intent-based prompt-refinement framework that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives.
Outcome:	The proposed framework outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses.

Copied to clipboard

Gaurav Arora, Shreya Jain, Srujana Merugu

On the Vector Representation of Utterances in Dialogue Context (L18-1)

Challenge:	Traditional approaches to intent detection struggle with out-of-scope (OOS) detection.
Approach:	They propose to use adaptive in-context learning and chain-of-thought prompting to detect intent in SOTA LLMs.
Outcome:	The proposed system achieves 2% of native accuracy with 50% less latency.

Copied to clipboard

Louisa Pragst, Niklas Rach, Wolfgang Minker, Stefan Ultes

Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (D19-1)

Challenge:	In recent years, the representation of words as vectors in a vector space has gained a high degree of attention in the research community.
Approach:	They introduce a new language resource that represents dialogue utterances in vector space and captures the semantic meaning of those utterrances in the dialogue context.
Outcome:	The proposed model captures relevant semantic information by comparing them to manually annotated dialogue acts.

Copied to clipboard

Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, Pascale Fung

Low-Resource NLP

Challenge:	a lack of research on multilingual or cross-lingual task-oriented dialog systems has limited results . we propose a zero-shot adaptation of task-orientated dialog systems to low-resource languages . task-focused systems are often trained with monolingual datasets that are expensive to build or acquire .
Approach:	They propose a zero-shot adaptation of multilingual task-oriented dialog systems to low-resource languages using latent variables and a set of very few parallel word pairs.
Outcome:	The proposed model performs better in natural language understanding task compared to state-of-the-art model . the proposed model uses very few parallel word pairs to refine cross-lingual representations .

Towards Open Environment Intent Prediction (2023.findings-acl)

Copied to clipboard

Yunhua Zhou, Jiawei Hong, Xipeng Qiu

Information Extraction & Text Mining

Challenge:	Out-of-Domain (OOD) Intent Classification and New Intent Discovering are two tasks in the Task-Oriented Dialogue System.
Approach:	They propose a task paradigm to extend Out-of-Domain (OOD) Intent Classification and New Intent Discovering tasks in the Task-Oriented Dialogue System.
Outcome:	The proposed scheme improves on existing OOD intent classification and discovery datasets.

AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages (2026.eacl-long)

Copied to clipboard

Kosei Uemura, Miaoran Zhang, David Ifeoluwa Adelani

Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)

Challenge:	Text embeddings are an essential building component of several NLP tasks.
Approach:	They propose a regional expansion of MTEB covering 59 languages, 14 tasks, and 38 datasets, including six newly added datasets.
Outcome:	The proposed model outperforms baselines and mE5 in hate speech detection, intent detection, and emotion classification tasks.

Copied to clipboard

Bingfeng Luo, Yansong Feng, Zheng Wang, Songfang Huang, Rui Yan, Dongyan Zhao

Challenge:	Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available.
Approach:	They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN.
Outcome:	The proposed approach significantly improves learning effectiveness when a small number of training examples are available.

Multi3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue (2023.findings-acl)

Copied to clipboard

Nikita Moghe, Evgeniia Razumovskaia, Liane Guillou, Ivan Vulić, Anna Korhonen, Alexandra Birch

Modeling with Recurrent Neural Networks for Open Vocabulary Slots (C18-1)

Challenge:	Task-oriented dialogue systems are typically constructed for a single domain or language and do not generalise well beyond this.
Approach:	They constructed a multilingual, multi-intent, multi domain dataset to support work on Natural Language Understanding (NLU) in ToD across multiple languages and domains simultaneously.
Outcome:	The proposed dataset extends the English-only dataset to include manual translations into a range of high, medium, and low resource languages in two domains (banking and hotels).

Copied to clipboard

Jun-Seong Kim, Junghoe Kim, SeungUn Park, Kwangyong Lee, Yoonju Lee

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)

Challenge:	Existing approaches to filling slots that take on values from a virtually unlimited set have been lacking in the natural language area.
Approach:	They propose a new attention-based recurrent neural network (RNN) model that captures the concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of . sentence.
Outcome:	The proposed model outperforms existing models with respect to discovering ‘open-vocabulary’ slots without any external information, such as a named entity database or knowledge base.

Copied to clipboard

Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade (2024.naacl-long)

Challenge:	Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks .
Approach:	They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain.
Outcome:	The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings.

Copied to clipboard

Zefan Cai, Xin Zheng, Tianyu Liu, Haoran Meng, Jiaqi Han, Gang Yuan, Binghuai Lin, Baobao Chang, Yunbo Cao

Learning Spoken Language Representations with Neural Lattice Language Modeling (2020.acl-main)

Challenge:	Existing models for natural language understanding are based on a well-defined intent 1 ontology.
Approach:	They propose to retrain the natural language understanding model as new data from real users are merged into existing data.
Outcome:	The proposed model shows that the semantically entangled intents can be recognized with an automatic workflow.

Copied to clipboard

Chao-Wei Huang, Yun-Nung Chen

Zero-shot User Intent Detection via Capsule Neural Networks (D18-1)

Challenge:	Existing methods for pretraining language models do not consider spoken language properties.
Approach:	They propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks.
Outcome:	The proposed framework outperforms baselines on spoken inputs on intent detection and dialogue act recognition datasets.

Copied to clipboard

Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip Yu

Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning (2024.lrec-main)

Challenge:	Existing methods to classify intents are labor-intensive and time-consuming as intents will be diverse and new intents may be involved.
Approach:	They propose a zero-shot intent detection problem which aims to detect emerging user intents where no labeled utterances are currently available.
Outcome:	The proposed model can discriminate emerging intents when no labeled utterances are available in training data.

Copied to clipboard

Saedeh Tahery, Sahar Kianian, Saeed Farzi

HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System (2022.coling-1)

Challenge:	Low-resource languages and computational expenses pose significant challenges in the domain of large language models.
Approach:	They propose a novel approach that uses adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models.
Outcome:	The proposed approach excels in zero-shot scenarios for Latin languages like Spanish, but fails to perform for languages distant from English, such as Thai and Persian.

Copied to clipboard

Zhanyu Ma, Jian Ye, Xurui Yang, Jianfeng Liu

Evaluating Pixel Language Models on Non-Standardized Languages (2025.coling-main)

Challenge:	Existing methods to train task-oriented dialogue systems in monolingual datasets are expensive to build.
Approach:	They propose a hierarchical framework to classify intents in high-level and slot filling in low-level . they incorporate sentence-level alignment among different languages to enhance intent detection .
Outcome:	The proposed framework achieves the performance on a public task-oriented dialog dataset.

Copied to clipboard

Alberto Muñoz-Ortiz, Verena Blaschke, Barbara Plank

Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)

Challenge:	pixel-based models can be used to transfer learning from standard languages to dialects . pretrained language models achieve strong results for languages seen during training, but their performance declines with out-of-domain dialects.
Approach:	They compare pixel-based models to token-based ones to evaluate dialects . standard german is tokenized in a more meaningful way, whereas the Bern dialect is tokenize in pixel form .
Outcome:	The proposed models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German.

Copied to clipboard

Xu Cao, Deyi Xiong, Chongyang Shi, Chao Wang, Yao Meng, Changjian Hu

Semantic Parsing

Challenge:	Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions .
Approach:	They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples.
Outcome:	The proposed model achieves significantly higher scores and improves robustness on two datasets.

Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)

Copied to clipboard

Lei Zhang, Runze Wang, Jingbo Zhou, Jingsong Yu, Zhenhua Ling, Hui Xiong

INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages (2025.acl-long)

Challenge:	Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries.
Approach:	They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service.
Outcome:	The proposed framework outperforms baseline methods with a significant margin.

Copied to clipboard

Hao Yu, Jesujoba Oluwadara Alabi, Andiswa Bukula, Jian Yun Zhuang, En-Shiun Annie Lee, Tadesse Kebede Guge, Israel Abebe Azime, Happy Buzaaba, Blessing Kudzaishe Sibanda, Godson Koffi Kalipe, Jonathan Mukiibi, Salomon Kabongo Kabenamualu, Mmasibidi Setaka, Lolwethu Ndolela, Nkiruka Odu, Rooweither Mabuya, Shamsuddeen Hassan Muhammad, Salomey Osei, Sokhar Samb, Dietrich Klakow, David Ifeoluwa Adelani

Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (D19-1)

Challenge:	Slot-filling and intent detection tasks are well-established tasks in Conversational AI, but current benchmarks for these tasks rely on evaluations of low-resource languages and translations from English benchmarks.
Approach:	They propose to use a multilingual, open-source benchmark dataset for 16 African languages with utterances generated by native speakers across diverse domains.
Outcome:	The proposed dataset compares multilingual transformer models and prompting large language models (LLMs) with the English language.

Copied to clipboard

Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran

Multimodality

Challenge:	Existing studies on text-image content have focused on image as primary content, and text as secondary content.
Approach:	They propose a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies . they show that employing both text and image improves intent detection by 9.6 .
Outcome:	The proposed model shows that using both text and image improves intent detection by 9.6 compared to using only the image modality.

A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (P19-1)

Copied to clipboard

Haihong E, Peiqing Niu, Zhongfu Chen, Meina Song

ResFormer: All-Time Reservoir Memory for Long Sequence Classification (2025.emnlp-main)

Challenge:	Existing models for slot filling and intent detection lack bi-directional interrelated connections between the intent and slots.
Approach:	They propose a bi-directional interrelated model for slot filling and intent detection that uses an SF-ID network to establish direct connections between the two tasks to promote each other mutually.
Outcome:	The proposed model improves on ATIS and Snips datasets in sentence-level semantic frame accuracy and improves performance on the two tasks.

Copied to clipboard

Hongbo Liu, Jia Xu

Information Retrieval

Challenge:	Existing models with quadratic time and memory complexity restrict input length . however, analyzing extensive sequential contexts is challenging .
Approach:	They propose a neural network architecture that captures contextual dependencies in linear time and a nonlinear readout to model short-term dependencies within sentences.
Outcome:	The proposed model outperforms baseline models on EmoryNLP datasets and on IEMOCAP and MultiWOZ datasets.

PCMID: Multi-Intent Detection through Supervised Prototypical Contrastive Learning (2023.findings-emnlp)

Copied to clipboard

Yurun Song, Junchen Zhao, Spencer Koehler, Amir Abdullah, Ian Harris

Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)

Challenge:	Existing approaches to intent detection assume that each utterance represents only a single intent.
Approach:	They propose a framework for intent detection that can learn multiple representations of a given user utterance under the context of different intent labels in an optimized semantic space.
Outcome:	The proposed framework achieves state-of-the-art on multiple public benchmark datasets and a private real-world dataset for the multi-intent detection task.

Copied to clipboard

Yejin Yoon, Yuri Son, Namyeong So, Minseo Kim, Minsoo Cho, Chanhee Park, Seungshin Lee, Taeuk Kim

Generate then Refine: Data Augmentation for Zero-shot Intent Detection (2024.findings-emnlp)

Challenge:	Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows.
Approach:	They propose a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows.
Outcome:	The proposed dataset outperforms baseline models in intent detection and mode transition handling.

Copied to clipboard

I-Fan Lin, Faegheh Hasibi, Suzan Verberne

Responsible & Trustworthy NLP

Challenge:	Existing data augmentation methods rely on few labelled examples for each intent category, which can be expensive in settings with many possible intents.
Approach:	They propose a data augmentation method for intent detection in zero-resource domains by using an open-source large language model and a smaller sequence-to-sequence model.
Outcome:	The proposed method significantly improves the data utility and diversity over the zero-shot LLM baseline for unseen domains and over common baseline approaches.

A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents (2024.findings-emnlp)

Copied to clipboard

Ankan Mullick, Sombit Bose, Abhilash Nandy, Gajula Chaitanya, Pawan Goyal

Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (2024.lrec-main)

Challenge:	Existing research focuses on simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents.
Approach:	They propose a multi-label multi-class intent detection dataset curated from existing benchmarks and a pointer network-based architecture to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets.
Outcome:	The proposed system outperforms baseline approaches in terms of accuracy and F1-score.

Copied to clipboard

Hao Lang, Yinhe Zheng, Binyuan Hui, Fei Huang, Yongbin Li

RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education (2024.lrec-main)

Challenge:	Existing methods for OOD intent detection are limited to single dialogue turns.
Approach:	They propose a context-aware OOD intent detection framework to model multi-turn contexts in OOD context detection tasks using unlabeled data.
Outcome:	The proposed framework improves the F1-OOD score by 29% on multi-turn OOD detection tasks compared to the previous best method.

Copied to clipboard

Jieun Han, Haneul Yoo, Junho Myung, Minsun Kim, Tak Yeon Lee, So-Yeon Ahn, Alice Oh

Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection (2025.emnlp-main)

Challenge:	generative AI is expanding in education, yet empirical analyses of large-scale and real-world interactions between students and AI systems remain limited.
Approach:	They present a dataset based on a semester-long experiment with 212 college students in English as Foreign Language (EFL) writing courses.
Outcome:	The proposed dataset includes conversation logs, students’ intent, students' self-rated satisfaction, and students’ essay edit histories.

Copied to clipboard

Ankan Mullick, Saransh Sharma, Abhik Jana, Pawan Goyal